| name | mta-lightspeed |
| metadata | {"author":"rhoai3-coding-demo","version":"1.0.0","platform-family":"mta","platform-baseline":"repo","ocp-baseline":"repo","skill-group":"Application Modernization"} |
| description | Use when configuring or using Red Hat Developer Lightspeed for MTA to modernize applications with AI assistance, including LLM provider configuration, code migration suggestions, and AI-assisted analysis. Do NOT use for installing MTA (use mta-install) or non-AI tool usage (use mta-cli/mta-ui).
|
MTA Developer Lightspeed
Use this skill for configuring and using Red Hat Developer Lightspeed for
Migration Toolkit for Applications (MTA) 8.2 — the AI-assisted code migration
component that integrates LLMs with MTA static analysis.
Source Grounding
Read references/source-capture.md before using product configuration details.
Official Red Hat docs are product authority. This skill covers LLM provider
configuration, Solution Server setup, Agent AI mode, VS Code extension settings,
and AI-assisted code resolution workflows.
Support Posture
Important: Developer Lightspeed for MTA and the Solution Server are
Technology Preview features. They are not supported with Red Hat production
SLAs and might not be functionally complete. Do not use them in production.
Concepts
Developer Lightspeed for MTA
Starting from MTA 8.0.0, the VS Code extension integrates with LLMs through the
Developer Lightspeed component. It applies LLM-driven code changes to resolve
issues found through static code analysis of Java applications, using Retrieval
Augmented Generation (RAG) for context-based resolutions.
Solution Server
An optional component that builds collective memory of source code changes
across analyses. It stores solved examples, computes success metrics (confidence
levels), and improves migration hints over successive migration waves. Requires
the LLM proxy service and a 5 Gi RWO persistent volume.
Agent AI
An automated analysis mode that plans context, selects sub-agents, and iterates
through code fixes. Accepts changes, recompiles, and re-analyzes in a loop
until all issues are resolved or a maximum of two attempts per issue is reached.
Supported LLM Providers
| Provider (Tackle CR value) | Example models |
|---|
| OpenShift AI platform | OpenAI-compatible models deployed on-cluster |
OpenAI (openai) | gpt-4, gpt-4o, gpt-4o-mini, gpt-3.5-turbo |
Azure OpenAI (azure_openai) | gpt-4, gpt-35-turbo |
Amazon Bedrock (bedrock) | anthropic.claude-3-5-sonnet-*, meta.llama3-1-70b-instruct-* |
Google Gemini (google) | gemini-2.0-flash-exp, gemini-1.5-pro |
Ollama (ollama) | llama3.1, codellama, mistral |
Workflow
- Read
references/source-capture.md and confirm the product baseline.
- Read
references/official-doc-extraction.md for detailed procedures.
- Select a deployment workflow:
- Agent AI without LLM proxy — direct LLM connection via
provider-settings.yaml.
- LLM proxy with optional Solution Server — centralised key management
via Tackle CR proxy service.
- Configure LLM API key Secret in
openshift-mta namespace.
- Configure Tackle CR with provider and model settings.
- Configure the VS Code extension (
provider-settings.yaml, GenAI settings).
- Run analysis and request AI-assisted code resolutions.
LLM API Key Secret
oc create secret generic kai-api-keys -n openshift-mta \
--from-literal=OPENAI_API_KEY='<YOUR_KEY>'
Provider-specific variants exist for Azure, Bedrock, Google, and
OpenShift AI. See references/official-doc-extraction.md.
Tackle CR Configuration
kind: Tackle
apiVersion: tackle.konveyor.io/v1alpha1
metadata:
name: mta
namespace: openshift-mta
spec:
kai_llm_proxy_enabled: true
kai_solution_server_enabled: true
kai_llm_provider: <provider-name>
kai_llm_model: <model-name>
Validation
oc get deploy,svc -n openshift-mta | grep -E 'kai-(api|db|importer)'
Data Privacy
Code snippets are transmitted intact to the configured LLM. No automatic
sanitisation or redaction is performed. Organisations with strict IP protection
should deploy self-managed models on OpenShift AI or RHEL AI and point
Developer Lightspeed at the private endpoint.
VS Code Extension Settings
Key settings in Extensions > MTA:
| Setting | Purpose |
|---|
| Gen AI: Enabled | Enable AI-assisted code fixes (default: true) |
| Gen AI: Agent mode | Enable automated agentic analysis loop |
| Gen AI: Excluded diagnostic sources | Skip specific diagnostic sources in Agent AI |
| Cache directory | Store cached LLM responses |
| Trace enabled / Trace directory | Trace MTA–LLM communication for debugging |
Debugging
- Extension logs:
Developer: Open Extension Logs Folder >
redhat.mta-vscode-extension/extension.log.
- Output panel: select
Red Hat Developer Lightspeed for MTA.
- Archive logs:
MTA: Generate Debug Archive in Command Palette.
Related Skills
mta-cli — CLI-based application analysis and migration.mta-ui — web UI analysis and assessment workflows.mta-install — MTA Operator installation and Tackle CR management.mta-vscode — VS Code extension non-AI features.mta-intellij — IntelliJ IDEA plugin workflows.
References
references/source-capture.mdreferences/official-doc-extraction.md
